NarrativeTrack

This is the official release of the dataset for the paper NarrativeTrack: Evaluating Entity-Centric Reasoning for Narrative Understanding.

📄 Paper: https://arxiv.org/abs/2601.01095  |  🤗 Hugging Face Paper: https://huggingface.co/papers/2601.01095  |  💻 Project page: https://github.com/apple/ml-NarrativeTrack

NarrativeTrack is an evaluation benchmark for measuring how well video–language models track entities across long, narrative videos — and how prone they are to hallucinating identities, actions, outfits, and scenes when an entity appears, disappears, and reappears over time.

Each example presents a short video clip of a target entity together with a question that probes whether the model can correctly relate the entity's appearance at one point in the video to another point (e.g. "Is the person shown at the end the same person who was shown behind fences earlier?"). Distractors are drawn from real co-occurring entities as well as synthetically perturbed attributes, making the benchmark a stress test for cross-time entity consistency.

This is an evaluation-only benchmark: it provides a single test split (no training split). The clips are sourced from AVA, Video-MME, and LVBench.

Dataset Statistics

• Examples: 1,006 QA pairs
• Videos: 406 unique entity clips
• Sources: Video-MME (509), AVA (282), LVBench (215)
• Question types: binary (478), mc / multiple-choice (446), ordering (82)
• Tracking types: appear (271), reappear (586), disappear (149)
• Dimensions: entity_existence, entity_ambiguity, entity_action_changes, entity_outfit_changes, entity_scene_changes

Data Fields

Each record in narrativetrack_qa.json contains:

Field	Type	Description
id	int	Unique question id
video_path	str	Path to the entity clip, relative to the dataset root (e.g. videos/AVA/1j20qq1JyX4/entity2_3/appear/1320_1350.mp4). After extracting videos.tar this path resolves directly.
question	str	The question shown to the model (answer options are included inline for mc and ordering).
answer	str	Ground-truth answer. Yes/No for binary, an option letter (a–d) for mc, and a comma-separated option ordering (e.g. a,c,b) for ordering.
question_type	str	One of binary, mc, ordering.
dimension	str	The reasoning dimension being probed (existence, ambiguity, action / outfit / scene changes).
track_type	str	Entity tracking event: appear, reappear, disappear.
template_type	str	Temporal framing of the question (e.g. later_to_start, start_to_later, agnostic).
template	str	The slot-filled template the question was generated from.
entity_id	str	Identifier of the target entity within the source video.
distractor_type	str	Source of distractor options: real, synthetic, or mix.

Download Videos

from huggingface_hub import hf_hub_download
import tarfile
import shutil

file_path = hf_hub_download(
    repo_id="hjha/NarrativeTrack",
    filename="videos.tar",
    repo_type="dataset",
)

print("Downloaded to:", file_path)
shutil.copy(file_path, "./videos.tar")

with tarfile.open("videos.tar", "r") as tar:
    tar.extractall(path=".")
# This creates ./videos/... matching the `video_path` field in each record.

Load Dataset

from datasets import load_dataset

# Evaluation benchmark: only a `test` split is available.
ds = load_dataset("hjha/NarrativeTrack")["test"]

example = ds[0]
print(example["question"])
print("answer:", example["answer"])
print("video:", example["video_path"])  # e.g. videos/AVA/.../1320_1350.mp4

Or, as a PyTorch Dataset that resolves video_path against the extracted videos:

import os
from datasets import load_dataset
from torch.utils.data import Dataset


class NarrativeTrackDataset(Dataset):
    def __init__(self, video_root="."):
        # Point `video_root` at the directory where you extracted videos.tar
        # (the one that contains the `videos/` folder).
        self.video_root = video_root
        self.data = load_dataset("hjha/NarrativeTrack")["test"]

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        sample = dict(self.data[idx])
        sample["video_path"] = os.path.join(self.video_root, sample["video_path"])
        assert os.path.exists(sample["video_path"]), \
            f"Video not found: {sample['video_path']}"
        return sample


test_ds = NarrativeTrackDataset(video_root=".")
example = test_ds[0]

Files

• narrativetrack_qa.json — the 1,006 QA records (test split).
• videos.tar — the 406 entity clips, stored under videos/<source>/... with paths matching the video_path field.

Citation

If you use NarrativeTrack in your research, please cite:

@article{narrativetrack2026,
  title   = {NarrativeTrack: Evaluating Entity-Centric Reasoning for Narrative Understanding},
  journal = {arXiv preprint arXiv:2601.01095},
  year    = {2026},
  url     = {https://arxiv.org/abs/2601.01095}
}